Conversation-sentence generation device, conversation-sentence generation method, and conversation-sentence generation program

ABSTRACT

A conversation-sentence generation device according to the invention of this application includes: an input unit that receives, as input information, a conversation sentence given from a user to an agent, and clue information based on which a physical and psychological state of the agent is estimated; an agent state storing unit that stores the physical and psychological state of the agent as an agent state; an agent state estimating unit that estimates a new agent state based on the input information and the agent state; an utterance intention generating unit that generates, based on the input information and the agent state, an utterance intention directed from the agent to the user; a conversation sentence generating unit that generates, based on the input information, the agent state, and the utterance intention, a conversation sentence given from the agent to the user; and an output unit that outputs the conversation sentence generated by the conversation sentence generating unit.

TECHNICAL FIELD

The present invention relates to a conversation-sentence generationdevice, a conversation-sentence generation method, and aconversation-sentence generation program, and more particularly to aconversation-sentence generation device, a conversation-sentencegeneration method, and a conversation-sentence generation program forgenerating a conversation sentence of a virtual agent having apersonified conversation with a user.

BACKGROUND ART

A human has a desire to communicate with somebody, and gain somebody'ssympathy. The communication target in this case is not limited to acertain human, but may be all possible types of targets such as amachine and an animal. Up to the present time, various types of dialoguesystems have been proposed as systems capable of realizing interactionbetween a human and a machine.

Patent Literature 1 is an example of such dialogue systems. According toa dialogue system disclosed in PTL 1, which is a system for realizing asmooth dialogue between a human and a machine, an ego-state estimatingunit estimates an ego-state through transactional analysis (for example,Mineyasu SUGITA, “Transactional Analysis”, Nihon Bunka Kagakusya Co.,Ltd., 1985), and a dialogue control unit outputs a response text basedon the estimated ego-state.

CITATION LIST Patent Literature PTL1: Japanese Patent Laid-open No.2006-71936 SUMMARY OF INVENTION Technical Problem

However, these conventional dialogue systems are intended to achieve apredetermined task through dialogues between a human and a machine basedon a scenario determined beforehand. In this case, most of dialoguesgenerated by the dialogue systems are uniform, and not intended to begiven as free conversations such as chatting between humans.

According to the conventional dialogue systems between a human and amachine, the dialogue control unit determines contents of a requestissued from a human, and converses with the human based on a dialoguescenario appropriate for the contents of the request to achieve apredetermined task. Most of dialogues generated in this manner areuniform, and generation of a wide variety of conversation sentences suchas conversations between humans, and generation of conversationsentences suited for situations of a user have been both difficult. Incase of a conversation between humans, various types of utterance aregiven even for making a remark having the same intention so as not let aconversation partner get tired of the conversation. In addition,utterance suited for a physical and psychological state of theconversation partner is given during the conversation. Furthermore, theconversation is consistent with contents of previous remarks given inthe past and remembered. However, it is difficult for the conventionaldialogue systems to realize human-like conversations of this level.

The present invention has been developed to solve the aforementionedproblems. The object of the present invention is to provide aconversation-sentence generation device, a conversation-sentencegeneration method, and a conversation-sentence generation programcapable of realizing human-like conversations.

Solution to Problem

The present invention is directed to a conversation-sentence generationdevice that generates a conversation sentence of a virtual agent havinga personified conversation with a user, including: an input unit thatreceives, as input information, a conversation sentence given from theuser to the agent, and clue information based on which a physical andpsychological state of the agent is estimated; an agent state storingunit that stores the physical and psychological state of the agent as anagent state; an agent state estimating unit that estimates a new agentstate based on the input information and the agent state; an utteranceintention generating unit that generates, based on the input informationand the agent state, an utterance intention directed from the agent tothe user; a conversation sentence generating unit that generates, basedon the input information, the agent state, and the utterance intention,a conversation sentence given from the agent to the user; and an outputunit that outputs the conversation sentence generated by theconversation sentence generating unit.

According to the present invention having this configuration, generationof a conversation sentence is divided into three phases: stateestimation, utterance intention generation, and conversation sentencegeneration. The utterance intention generation and the conversationsentence generation are separately handled so that a plurality ofconversation sentences can be generated for an identical utteranceintention. This method allows generation of a wide variety ofconversation sentences. The agent state or the user state is estimatedto estimate a physical state or psychological state of the user oragent. This method allows generation of conversation sentences suitedfor the estimated physical and psychological state. The results of thestate estimation are stored in the state storing unit. This methodallows generation of conversation sentences consistent with contents ofprevious remarks with reference to the stored results of the stateestimation.

The present invention is directed to a conversation-sentence generationmethod that generates a conversation sentence of a virtual agent havinga personified conversation with a user, including: receiving, as inputinformation, a conversation sentence given from the user to the agent,and clue information based on which a physical and psychological stateof the agent is estimated; storing the physical and psychological stateof the agent as an agent state; estimating a new agent state based onthe input information and the agent state; generating, based on theinput information and the agent state, an utterance intention directedfrom the agent to the user; generating, based on the input information,the agent state, and the utterance intention, a conversation sentencegiven from the agent to the user; and outputting the generatedconversation sentence by the conversation sentence generating unit.

The present invention is directed to a program allowing a computer toexecute: a process that receives, as input information, a conversationsentence given from a user to an agent, and clue information based onwhich a physical and psychological state of the agent is estimated; aprocess that stores the physical and psychological state of the agent asan agent state; a process that estimates a new agent state based on theinput information and the agent state; a process that generates, basedon the input information and the agent state, an utterance intentiondirected from the agent to the user; a conversation sentence generatingprocess that generates, based on the input information, the agent state,and the utterance intention, a conversation sentence given from theagent to the user; and a process that outputs the conversation sentencegenerated by the conversation sentence generating process.

Advantageous Effects of Invention

According to the present invention, conversation sentences realizinghuman-like conversations are generated.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a configuration of a firstexemplary embodiment of the present invention.

FIG. 2 is a block diagram illustrating a configuration of a secondexemplary embodiment of the present invention.

FIG. 3 is a flowchart describing operation of the exemplary embodimentsof the present invention.

DESCRIPTION OF EMBODIMENTS

Exemplary embodiments according to the present invention are hereinafterdescribed with reference to the drawings. The present invention relatesto a system which handles a machine or an animal as a personified agent,and realizes a conversation between the machine or animal and a humancorresponding to a user.

First Exemplary Embodiment

FIG. 1 is a block diagram illustrating a configuration example of aconversation-sentence generation device according to a first exemplaryembodiment. The device according to the first exemplary embodiment ofthe present invention is provided with an input unit 1, an agent stateestimating unit 2, an utterance intention generating unit 3, aconversation sentence generating unit 4, an output unit 5, and an agentstate storing unit 6.

The input unit 1 receives, as input information, a conversation sentencegiven from a user to an agent, and clue information based on which aphysical and psychological state of the agent is estimated, andtransmits the input information to the agent state estimating unit 2.

The input information includes a pair of attribute name and attributevalue. The input information may contain the conversation sentence givenfrom the user to the agent without change, or contain only a main pointof the conversation sentence extracted based on analysis. For example,when the user transmits an email saying, “(Coming back) late” to theagent, the input information may contain only the main point of “email”as the attribute name, and “late” as the attribute value. In addition,when the agent expresses its own state and starts a conversation, theconversation sentence from the user need not be input. Other examples ofthe input information include attributes peculiar to the user and theagent, such as nicknames and genders of the user and the agent(hereinafter referred to as user attributes and agent attributes), anddynamic attributes such as time and weather at the time of generation ofthe conversation sentence (hereinafter referred to as dynamicattributes). Table 1, Table 2, and Table 3 show examples of the inputinformation.

TABLE 1 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME MAMMY GENDER FEMALE

TABLE 2 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 3 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUEEMAIL LATE TIME ZONE EVENING

The agent state estimating unit 2 estimates a new agent state based onthe input information received from the input unit 1, and an agent statestored in the agent state storing unit 6. The agent state estimatingunit 2 stores the estimated agent state in the agent state storing unit6, and transmits the input information to the utterance intentiongenerating unit 3.

The agent state represents a physical and psychological state of theagent, and is expressed by the pair of attribute name and attributevalue similarly to the input information. For example, an “emotionvalue” representing an emotion of the agent using a numerical valueindicates the “emotion value” as a positive value when the agent ishappy or having fun. On the other hand, the “emotion value” becomes anegative value when the agent is sad or in pain. In both cases, theintensity of the emotion is defined by the absolute value of eachemotion value.

The agent state is estimated based on a state estimation rule. The stateestimation rule is constituted by a condition part and a statedescription part. The state description part describes a physical andpsychological state of the agent. The condition part describes acondition set for determining whether or not the agent is in the statedescribed in the state description part based on the input informationand the agent state stored in the agent state storing unit 6. When theinput information and the agent state match with the condition part, itis estimated that the agent is in the agent state described in the statedescription part. Table 4 shows an example of the state estimation rule.

TABLE 4 EXAMPLE OF STATE ESTIMATION RULE (A STATE: AGENT STATE, U STATE:USER STATE) CONDITION PART STATE DESCRIPTION PART DYNAMIC ATTRIBUTE -> ASTATE -> EMOTION VALUE = −1 EMAIL = LATE DYNAMIC ATTRIBUTE -> U STATE ->POSITIVE-NEGATIVE EMAIL = OVERTIME WORK STATE = NEGATIVE

The utterance intention generating unit 3 generates an utteranceintention directed from the agent to the user based on the inputinformation received from the agent state estimating unit 2 and theagent state, and transmits the generated utterance intention and theinput information to the conversation sentence generating unit 4. Theutterance intention is defined by a label such as “lonelinessexpression” and “user comfort”, and a score indicating the intensity ofthe intention. The utterance intention generating unit 3 generates oneor a plurality of utterance intentions per generation of a conversationsentence.

The utterance intention is generated based on an utterance intentiongeneration rule. The utterance intention generation rule is constitutedby a condition part and an utterance intention description part. Theutterance intention description part describes an utterance intentiondirected from the agent to the user. The condition part describes acondition set for determining whether or not the agent is in theutterance intention described in the utterance intention descriptionpart based on the input information, the agent state and a user state.When the input information and the agent state match with the conditionpart, the utterance intention described in the utterance intentiondescription part is generated. The score of the utterance intention isthe sum of the scores given to the condition part. In case of anutterance intention generated immediately after a state change, it isconsidered that the intensity of the intention concerning the stateafter the change is high. In this case, a bonus point may be given tothe score of the condition associated with the state after the change toraise the score of the corresponding intention when the utteranceintension is generated within a threshold period from the state change.

TABLE 5 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) UTTERANCE INTENTION CONDITION PARTDESCRIPTION PART U STATE -> POSITIVE-NEGATIVE USER COMFORT STATE =NEGATIVE (SCORE: 1.0) A STATE -> EMOTION = LONELY LONELINESS EXPRESSION(SCORE: 2.0)

The conversation sentence generating unit 4 generates a conversationsentence given from the agent to the user based on the input informationreceived from the utterance intention generating unit 3, the agentstate, and the utterance intension, and transmits the generatedconversation sentence to the output unit 5.

The conversation sentence is generated based on a conversation sentencegeneration rule. The conversation sentence generation rule isconstituted by a condition part and a conversation sentence descriptionpart. The conversation sentence description part describes aconversation sentence given from the agent to the user. The conditionpart describes a condition set for determining whether or not theconversation sentence described in the conversation sentence descriptionpart is appropriate for a conversation sentence to be given from theagent to the user based on the input information, the agent state, andthe utterance intention. When the input information, the agent state,and the utterance intention match with the condition part, aconversation sentence described in the conversation sentence descriptionpart is selected. The conversation sentence may be a sentence withoutchange, or described in template format where values of the userattributes, the agent attributes and the like are contained asvariables. In the latter case, the variable parts are converted intovalues of the user attributes, the agent attributes and the like at thetime of generation of the conversation sentence so that a sentencecontaining the user name and the agent name can be generated.

Generation of a conversation sentence is conducted for each utteranceintention so that one sentence can be produced from each utteranceintention. When a plurality of conversation sentence generation rulesmatch with one utterance intention, scores are summed for each of thecondition parts similarly to the case of intention generation, and therule having the largest total score is adopted as the conversationsentence generation rule. When a template recently selected iscontinuously used, the user becomes bored with the same responseproduced based on the same template. Accordingly, continuous selectionof the same rule may be avoided by imposing a penalty on the score ofthe rule used within a threshold period from the previous use.

TABLE 6 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONVERSATIONSENTENCE CONDITION PART DESCRIPTION PART UTTERANCE INTENTION = USER GOODLUCK WITH YOUR COMFORT (SCORE: 1.0) HARD OVERTIME WORK! U STATE ->SITUATION = DURING OVERTIME WORK (SCORE: 1.0), UTTERANCE INTENTION =USER [A ATTRIBUTE -> NICKNAME] COMFORT (SCORE: 1.0) IS ON YOUR SIDE, [UATTRIBUTE -> NICKNAME].

The output unit 5 outputs, to the user, the conversation sentencereceived from the conversation sentence generating unit 4. For example,the output unit 5 edits character colors and sizes of the conversationsentence, and transmits an email containing the conversation sentence,or contributes the conversation sentence to SNS (social networkingservice). Alternatively, the output unit 5 may present the conversationsentence to the user in voices using a voice synthesizer.

The agent state storing unit 6 stores the agent state estimated by theagent state estimating unit 2 in association with the time ofgeneration. In case of no change in association with state estimation,the agent state storing unit 6 retains the agent state at the time ofprevious generation of a conversation sentence so as to allow generationof a conversation sentence consistent with the previous conversationsentence.

TABLE 7 EXAMPLE OF CONTENTS OF STATE STORING UNIT (PREVIOUS STATECONTINUES IN NO-CHANGE PART) U STATE -> POSITIVE- A STATE -> A STATE ->TIME NEGATIVE STATE EMOTION EMOTION VALUE 1 NEGATIVE LONELY −1 2POSITIVE LONELY −1 3 POSITIVE HAPPY +2 . . .

Second Exemplary Embodiment

FIG. 2 is a block diagram illustrating a configuration example of aconversation-sentence generation device according to a second exemplaryembodiment. In the second exemplary embodiment of the present invention,a user state is estimated as well as an agent state. For realizingestimation of the user state in the second exemplary embodiment, a userstate estimating unit 22 and a user state storing unit 62 are furtheradded to the configuration illustrated in FIG. 1. The estimation and useof the user state are achieved in a manner similar to the method of theestimation and use of the agent state.

The user state represents a physical and psychological state of theuser. Examples of the user state include a “positive-negative state”having a “positive” attribute value or a “negative” attribute value. The“positive-negative state” expresses a mental state of the user by twovalues of a “positive” value and a “negative” value based on thecontents of an email given from the user or the like.

Operation executed according to the first and second exemplaryembodiments is hereinafter described in detail with reference to aflowchart shown in FIG. 3. Initially, the input unit 1 receives, asinput information, a conversation sentence given from the user to theagent, and clue information based on which the physical andpsychological state of the agent is estimated (Step A1).

Then, the agent state estimating unit 2, or an agent state estimatingunit 21 and the user state estimating unit 22, estimate a new agentstate and a new user state based on the input information received fromthe input unit 1, and an agent state stored in the agent state storingunit 6, or in an agent state storing unit 61 and the user state storingunit 62, and store the estimated agent state and user state in the agentstate storing unit 6, or in the agent state storing unit 61 and the userstate storing unit 62 (step A2).

Subsequently, the utterance intention generating unit 3 generates anutterance intention directed from the agent to the user based on theinput information, the agent state, and the user state received from theagent state estimating unit 2 (step A3).

Thereafter, the conversation sentence generating unit 4 generates aconversation sentence given from the agent to the user based on theinput information, the agent state, and the utterance intention receivedfrom the utterance intention generating unit 3 (step A4).

Finally, the output unit 5 outputs the conversation sentence (step A5),and ends processes.

Advantageous effects of the exemplary embodiments are hereinafterdescribed. According to the exemplary embodiments, the utteranceintention generating unit generates an utterance intention, and theconversation sentence generating unit generates a conversation sentencecorresponding to the generated utterance intention. In this case,variations of conversation sentences to be generated increase when aplurality of conversation sentence generation rules are prepared for oneutterance intention. In addition, the agent state estimating unit andthe user state estimating unit estimate physical and psychologicalstates of the agent and the user, and generate a conversation sentencein correspondence with the estimation. Accordingly, the conversationthus realized contains an emotion of the agent, or reflects a mentalstate of the user. Furthermore, the conversation sentence to begenerated becomes consistent with contents of previous remarks withreference to results of the state estimation stored in the state storingunit.

Described hereinafter are specific examples of operation according tothe best mode for carrying out the present invention. Discussed in theseexamples is a conversation system which realizes a conversation with adog kept as a pet and corresponding to the agent.

EXAMPLE 1

Discussed herein is generation of a conversation sentence when userattributes, agent attributes, and dynamic attributes shown in Table 11,Table 12, and Table 13 are given as input. Initially, the agent stateestimating unit 2 estimates a “situation” of the agent state as “housesitting”, and an “emotion” of the agent state as “lonely” based on inputof a dynamic attribute “email=late” with reference to an agent stateestimation rule shown in Table 14.

Then, the utterance intention generating unit 3 generates an utteranceintension of “loneliness expression” for house sitting with reference toan utterance intention generation rule shown in Table 15, based on theagent state of “emotion=lonely” determined by the agent state estimatingunit 2.

Subsequently, the conversation sentence generating unit 4 selects threetypes of templates shown in Table 16, and generates three types ofconversation sentences, with reference to a conversation sentencegeneration rule shown in Table 16 which indicates a condition match of“utterance intention=loneliness expression” and “situation=housesitting”. In an actual situation, only one conversation sentence isselected, wherefore one of the three conversation sentences is randomlyor sequentially generated to realize a wide variety of conversations notboring for the user.

According to this example, three patterns of the conversation sentencegeneration rule are prepared. However, when the number of the patternsto be prepared increases, the frequency of use of the same templatelowers, in which condition variations of conversations further increase.

TABLE 11 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME MAMMY GENDER FEMALE

TABLE 12 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 13 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUEEMAIL LATE TIME ZONE EVENING

TABLE 14 EXAMPLE OF AGENT STATE ESTIMATION RULE (A STATE: AGENT STATE, USTATE: USER STATE) CONDITION ESTIMATION RESULT DYNAMIC LATE A STATE ->SITUATION = HOUSE ATTRIBUTE -> SITTING EMAIL = A STATE -> EMOTION =LONELY

TABLE 15 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) CONDITION INTENTION A STATE -> EMOTION =LONELY LONELINESS (SCORE: 4.0) EXPRESSION

TABLE 16 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONDITIONTEMPLATE UTTERANCE [A ATTRIBUTE -> NICKNAME] FEELS INTENTION = LONELY ATHOME ALONE, ∘(;Δ;)∘ LONELINESS BOO-HOO! EXPRESSION YOU ARE LATE, (i 

 i) OH NO! (SCORE: 4.0) [A ATTRIBUTE -> NICKNAME]'S LONELY A STATE ->HEART IS BROKEN! (p_q) WAH! SITUATION = YOU ARE LATE! HOUSE SITTING SOLONELY WITH TEARS IN [A ATTRIBUTE -> NICKNAME]'S EYES! (T_T)

EXAMPLE 2

Discussed herein is generation of a conversation sentence when userattributes, agent attributes, and dynamic attributes shown in Table 21,Table 22, and Table 23 are given as input.

Initially, the agent state estimating unit 2 estimates a “situation” ofthe agent state as “house sitting”, and an “emotion value” as “−1” basedon input of a dynamic attribute “email=late” with reference to an agentstate estimation rule shown in Table 24. When the emotion value of theagent state is a “positive value (0 or larger)”, it is determined thatthe emotion of the agent is medium to good. In this case, the emotion ofthe agent state is estimated as “lonely”. When the emotion value of theagent state is a “negative value (−1 or smaller)”, it is determined thatthe emotion of the agent is bad. In this case, the emotion of the agentstate is estimated as “hate”.

Then, the utterance intention generating unit 3 generates an utteranceintention based on the “emotion” of the agent state with reference to anutterance intention generation rule shown in Table 25. The utteranceintention generating unit 3 generates an utterance intention as“loneliness expression” when “emotion=lonely”, and generates anutterance intention as “hate expression” when “emotion=hate”.

Subsequently, the conversation sentence generating unit 4 generates asentence expressing “lonely feeling” when “loneliness expression”, and asentence expressing “hate feeling” when “hate expression”, based ondefinition of templates matching with utterance intentions. Withreference to a conversation sentence generation rule shown in Table 26,a sentence “Koro feels lonely at home alone, o(;_;)o boo-hoo!” isgenerated when “utterance intention=loneliness expression”, or asentence “I hate house sitting!” is generated when “utteranceintention=hate expression”.

According to this example, the conversation sentence to be generated isvaried based on the “emotion value” defined as the state of the agent,so that the agent, which is not a human, can converse as if it hadhuman-like emotion.

TABLE 21 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME MAMMY GENDER FEMALE

TABLE 22 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 23 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUEEMAIL LATE TIME ZONE EVENING

TABLE 24 EXAMPLE OF AGENT STATE ESTIMATION RULE (A STATE: AGENT STATE, USTATE: USER STATE) CONDITION ESTIMATION RESULT DYNAMIC LATE A STATE ->SITUATION = ATTRIBUTE -> HOUSE SITTING EMAIL = A STATE -> EMOTION VALUE= −1 A STATE -> 0 OR LARGER A STATE -> EMOTION = EMOTION LONELY VALUE =−1 OR SMALLER A STATE -> EMOTION = HATE

TABLE 25 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) CONDITION INTENTION A STATE -> LONELY(SCORE: 4.0) LONELINESS EXPRESSION EMOTION = HATE (SCORE: 4.0) HATEEXPRESSION

TABLE 26 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONDITIONTEMPLATE UTTERANCE INTENTION = LONELINESS [A ATTRIBUTE -> EXPRESSION(SCORE: 4.0) NICKNAME] A STATE -> SITUATION = HOUSE SITTING FEELS LONELYAT HOME ALONE, ∘(;_;)∘ BOO-HOO! UTTERANCE INTENTION = HATE I HATE HOUSEEXPRESSION (SCORE: 4.0) SITTING! A STATE -> SITUATION = HOUSE SITTING

EXAMPLE 3

Discussed herein is generation of a conversation sentence when userattributes, agent attributes, and dynamic attributes shown in Table 31,Table 32, and Table 33 are given as input.

Initially, the agent state estimating unit 21 estimates a situation ofthe agent state as “house sitting”, and an emotion value as “−1” basedon input of a dynamic attribute “email=late” with reference to an agentstate estimation rule shown in Table 341. When the emotion value of theagent state is a “positive value (0 or larger)”, it is determined thatthe emotion of the agent is medium to good. In this case, the emotion ofthe agent state is estimated as “lonely”. When the emotion value of theagent state is a “negative value (−1 or smaller)”, it is determined thatthe emotion of the agent is bad. In this case, the emotion of the agentstate is estimated as “hate”.

In addition, the user state estimating unit 22 estimates that a mentalstate of the user is negative based on input of a dynamic attribute“user situation=during overtime work” which indicates the currentsituation of the user (during overtime work) with reference to a userstate estimation rule shown in Table 342. In this case, apositive-negative state of the user state is estimated as “negative”. Onthe other hand, when the situation of the user is estimated as apositive mental state for the user (such as dating and playing), thepositive-negative state of the user state is estimated as “positive”.

Subsequently, the utterance intention generating unit 3 generates anutterance intention based on the emotion of the agent state and thepositive-negative state of the user state with reference to an utteranceintention generation rule shown in Table 35.

The utterance intention generating unit 3 generates an utteranceintention “loneliness expression” when the agent state is“emotion=lonely”, and generates an utterance intention “hate expression”when the agent state is “emotion=hate”. In addition, the utteranceintention generating unit 3 generates an utterance intention “usercomfort” to comfort the user in a negative mental state when the userstate is “positive-negative state=negative”, and generates an utteranceintention “user's joy sympathy” to share joy with the user in a positivemental state when the user state is “positive-negative state=positive”.

Thereafter, the conversation sentence generating unit 4 generates aconversation sentence corresponding to each conversation intention withreference to a conversation sentence generation rule shown in Table 36.

For example, the following conversation sentence is generated when thereare given “loneliness expression” and “user comfort” as the utteranceintention, “situation=house sitting” and “emotion=lonely” as the agentstate, and “situation=during overtime work” as the user state.

KORO FEELS LONELY AT HOME ALONE, ∘(;Δ;)∘ BOO-HOO! BUT MAMMY IS WORKINGOVERTIME HARD, SO KORO WILL TRY MY BEST TO OVERCOME THIS HARD TIME,MAMMY!

According to this example, a conversation sentence to be generated isvaried based on the definition of the states of the user such as“positive-negative state”, wherefore generation of a conversationsentence expressed in a way expected by the user is allowed.

TABLE 31 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME MAMMY GENDER FEMALE

TABLE 32 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 33 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUEEMAIL LATE USER SITUATION DURING OVERTIME WORK TIME ZONE EVENING

TABLE 341 EXAMPLE OF AGENT STATE ESTIMATION RULE (A STATE: AGENT STATE)CONDITION ESTIMATION RESULT DYNAMIC LATE A STATE -> SITUATION =ATTRIBUTE -> HOUSE SITTING EMAIL = A STATE -> EMOTION VALUE = −1 A STATE-> 0 OR LARGER A STATE -> EMOTION = EMOTION LONELY VALUE = −1 OR SMALLERA STATE -> EMOTION = HATE

TABLE 342 EXAMPLE OF USER STATE ESTIMATION RULE (U STATE: USER STATE)CONDITION ESTIMATION RESULT DYNAMIC DURING U STATE -> POSITIVE-NEGATIVEATTRIBUTE -> OVERTIME STATE = NEGATIVE USER WORK U STATE -> SITUATION =DURING SITUATION OVERTIME WORK DATING U STATE -> POSITIVE-NEGATIVE STATE= POSITIVE U STATE -> SITUATION = DATING

TABLE 35 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) CONDITION INTENTION A STATE -> LONELY(SCORE: 4.0) LONELINESS EMOTION = EXPRESSION HATE (SCORE: 4.0) HATEEXPRESSION U STATE -> NEGATIVE (SCORE: 2.0) USER COMFORT POSITIVE-POSITIVE (SCORE: 2.0) USER'S JOY NEGATIVE SYMPATHY STATE =

TABLE 36 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONDITIONTEMPLATE (LONELINESS EXPRESSION + USER COMFORT) UTTERANCE INTENTION = [AATTRIBUTE -> NICKNAME] LONELINESS EXPRESSION (SCORE 4.0) FEELS LONELY ATHOME A STATE -> SITUATION = HOUSE ALONE, ∘(;Δ;)∘ BOO-HOO! SITTINGUTTERANCE INTENTION = USER BUT YOU ARE WORKING COMFORT (SCORE: 2.0)OVERTIME HARD, SO [A U STATE -> SITUATION = DURING ATTRIBUTE ->NICKNAME] OVERTIME WORK WILL TRY MY BEST TO A STATE -> EMOTION = LONELYOVERCOME THIS HARD TIME, [U ATTRIBUTE -> NICKNAME]! (HATE EXPRESSION +USER COMFORT) UTTERANCE INTENTION = HATE I HATE HOUSE SITTING!EXPRESSION (SCORE: 4.0) A STATE -> SITUATION = HOUSE SITTING UTTERANCEINTENTION = USER BUT YOU ARE ALWAYS WORKING COMFORT (SCORE: 2.0) HARDFOR [A ATTRIBUTE -> U STATE -> SITUATION = DURING NICKNAME], [UATTRIBUTE -> OVERTIME WORK NICKNAME]. [A ATTRIBUTE -> A STATE -> EMOTION= HATE NICKNAME] WILL PUT UP WITH THIS HARD TIME. DON'T WORK TOO HARD 

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(LONELINESS EXPRESSION + USER'S JOY SYMPATHY) UTTERANCE INTENTION = [AATTRIBUTE -> NICKNAME] LONELINESS EXPRESSION (SCORE: 4.0) FEELS LONELYAT HOME A STATE -> SITUATION = HOUSE ALONE, ∘(;Δ;)∘ BOO-HOO! SITTINGUTTERANCE INTENTION = USER'S JOY BUT I'M HAPPY TO HEAR SYMPATHY (SCORE:2.0) THAT, [U ATTRIBUTE -> U STATE -> SITUATION = DATING NICKNAME]. ASTATE -> EMOTION = LONELY [A ATTRIBUTE -> NICKNAME] WILL PUT UP WITHTHIS HARD TIME FOR [U ATTRIBUTE -> NICKNAME]'S HAPPINESS! (HATEEXPRESSION + USER'S JOY SYMPATHY) UTTERANCE INTENTION = HATE I HATEHOUSE SITTING! EXPRESSION (SCORE: 4.0) A STATE -> SITUATION = HOUSESITTING UTTERANCE INTENTION = USER'S OOHH! (—.—;) JOY SYMPATHY (SCORE:2.0) BUT [A ATTRIBUTE -> NICKNAME] U STATE -> SITUATION = DATING WILLPUT UP WITH THIS HARD A STATE -> EMOTION = HATE TIME FOR YOUR HAPPINESS,[U ATTRIBUTE -> NICKNAME]!

EXAMPLE 4

Discussed herein is generation of a conversation sentence when userattributes, agent attributes, and dynamic attributes shown in Table 41,Table 42, and Table 43 are given as input. This example shows aconversation between a plurality of users and the agent, as well as aone-to-one conversation between the user and the agent.

Initially, the agent state estimating unit 21 and the user stateestimating unit 22 generate a situation of the agent state as “waitingfor souvenir”, an emotion value as “+1”, and a degree of intimacy of theuser state as “+1” based on input of a dynamic attribute as“souvenir=food” from a user P1 with reference to an agent stateestimation rule and a user state estimation rule shown in Table 44. Whenthe emotion value of the user state is “threshold or larger (−2 orlarger)”, it is determined that the relation between the agent and theuser is medium to good. In this case, the emotion of the agent state“very happy” is generated. When the emotion value of the user state is“threshold or smaller (−3 or smaller)”, it is determined that therelation between the agent and the user is bad. In this case, theemotion of the agent state “happy” is generated.

On the other hand, the agent state estimating unit 21 and the user stateestimating unit 22 generate a situation of the agent state as “commutingto hospital”, an emotion value as “−2”, and a degree of intimacy of theuser state as “−2” based on input of a dynamic attribute “email=going tohospital” given from a user P2. When the emotion value of the user stateis “threshold or larger (−2 or larger)”, it is determined that therelationship between the agent and the user is medium to good. In thiscase, the emotion of the agent state “sad” is generated. When theemotion value of the user state is “threshold or smaller (−3 orsmaller)”, it is determined that the relationship between the agent andthe user is bad. In this case, the emotion of the agent state “hate” isgenerated.

Then, the utterance intention generating unit 3 generates an utteranceintention based on the agent state and the user state with reference toan utterance intention generation rule shown in Table 45. In case of theuser P1, the utterance intention generating unit 3 generates anutterance intention “delight expression” when “emotion=very happy”, andgenerates an utterance intention “joy expression” when “emotion=happy”.In case of the user P2, the utterance intention generating unit 3generates an utterance intention “sadness expression” when“emotion=sad”, and generates an utterance intention “hate expression”when “emotion=hate”.

Subsequently, the conversation sentence generating unit 4 generates aconversation sentence corresponding to each conversation intention whileconsidering the degree of intimacy between the user and the agent asconversation targets with reference to a conversation sentencegeneration rule shown in Table 46.

For example, for a user exhibiting a low degree of intimacy as a resultof repetitive negative actions for the agent, the sentence is defined asa stiff and formal response even when a positive dynamic attribute(“souvenir=food”) is given. On the other hand, for a user exhibiting ahigh degree of intimacy as a result of repetitive positive actions forthe agent, the sentence is defined as a response matching with theemotion of the agent, i.e., such a response as to fawn on the user evenwhen a negative dynamic attribute is given, both with a wide variety oftemplates so as to make responses matching with the emotion of theagent.

As noted above, the degrees of intimacy between the respective users andthe agent are defined by numerical values based on emotions of the agentproduced through exchanges between the respective users and the agent.The degree of intimacy is raised when a dynamic attribute positive forthe agent is given, and lowered when a dynamic attribute negative forthe agent is given. These degrees of intimacy are stored and managed foreach user. In this case, an emotion of the agent to be produced isvariable between a user exhibiting a high degree of intimacy and a userexhibiting a low degree of intimacy even when the same dynamic attributeis given. Accordingly, a response given to each user reflects the degreeof intimacy of the corresponding user.

TABLE 41 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUE (USERP1) NICKNAME MAMMY GENDER FEMALE (USER P2) NICKNAME HIRO-KUN GENDER MALE

TABLE 42 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 43 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUE(DYNAMIC ATTRIBUTES OF USER P1) SOUVENIR FOOD (DYNAMIC ATTRIBUTES OFUSER P2) EMAIL GOING TO HOSPITAL

TABLE 44 EXAMPLE OF AGENT STATE ESTIMATION RULE AND USER STATEESTIMATION RULE (A STATE: AGENT STATE, U STATE: USER STATE) CONDITIONESTIMATION RESULT DYNAMIC ATTRIBUTE -> A STATE -> SITUATION = WAITINGFOR SOUVENIR = FOOD SOUVENIR A STATE -> EMOTION VALUE+ = 1 U STATE ->DEGREE OF INTIMACY+ = 1 DYNAMIC ATTRIBUTE -> A STATE -> EMOTION = VERYHAPPY SOUVENIR = FOOD U STATE -> DEGREE OF INTIMACY ≧−2 DYNAMICATTRIBUTE -> A STATE -> EMOTION = HAPPY SOUVENIR = FOOD U STATE ->DEGREE OF INTIMACY≦−3 DYNAMIC ATTRIBUTE -> A STATE -> SITUATION =COMMUTING EMAIL = GOING TO HOSPITAL TO HOSPITAL A STATE -> EMOTIONVALUE− = 2 U STATE -> DEGREE OF INTIMACY− = 2 DYNAMIC ATTRIBUTE -> ASTATE -> EMOTION = SAD EMAIL = GOING TO HOSPITAL U STATE -> DEGREE OFINTIMACY ≧−2 DYNAMIC ATTRIBUTE -> A STATE -> EMOTION = HATE EMAIL =GOING TO HOSPITAL U STATE -> DEGREE OF INTIMACY ≦−3

TABLE 45 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) CONDITION INTENTION A STATE -> VERY HAPPYDELIGHT EXPRESSION EMOTION = (SCORE: 4.0) HAPPY (SCORE: 4.0) JOYEXPRESSION SAD (SCORE: 4.0) SADNESS EXPRESSION HATE (SCORE: 4.0) HATEEXPRESSION

TABLE 46 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONDITIONTEMPLATE (JOY EXPRESSION) UTTERANCE INTENTION = DELIGHT YEAH! I'VE GOTSOUVENIR! EXPRESSION (SCORE: 4.0) THANK YOU VERY VERY U STATE -> DEGREEOF INTIMACY ≧−2 MUCH! 

(*{circumflex over ( )}_{circumflex over ( )}*)v UTTERANCE INTENTION =JOY THANK YOU FOR YOUR EXPRESSION (SCORE: 4.0) SOUVENIR. U STATE ->DEGREE OF INTIMACY ≦−3 (SADNESS EXPRESSION, HATE EXPRESSION) UTTERANCEINTENTION = SADNESS BOO-HOO! (;_;) I DON'T WANT EXPRESSION (SCORE: 4.0)TO GO TO HOSPITAL. A STATE -> SITUATION = COMMUTING [U ATTRIBUTE ->NICKANE], TO HOSPITAL PLEASE DON'T TAKE ME U STATE -> INTIMACY ≧−2THERE! UTTERANCE INTENTION = HATE YOU'RE MEAN, [U ATTRIBUTE EXPRESSION(SCORE: 4.0) -> NICKANE]! I WON'T GO TO A STATE -> SITUATION = COMMUTINGHOSPITAL! TO HOSPITAL U STATE -> INTIMACY ≦−3

EXAMPLE 5

Discussed herein is an example of generation of a conversation sentencewhen user attributes, agent attributes, dynamic attributes shown inTable 51, Table 52, and Table 53 are given as input. This example showsgeneration of a conversation consistent with flow of a previousconversation.

It is assumed as a situation that the agent is hungry at a time of input1, and fully fed at input 2. It is assumed under this situation thatinput 3 or input 4 is given.

Initially, the agent state estimating unit 21 and the user stateestimating unit 22 generate a situation of the agent state as “housesitting”, an emotion value as “−1”, and an emotion as “lonely” based oninput of a dynamic attribute “email=late” at input 1.

On the other hand, the agent state estimating unit 21 and the user stateestimating unit 22 generate a positive-negative state of the user stateas “positive” based on input of a dynamic attribute “usersituation=dating”. In addition, the agent state estimating unit 21 andthe user state estimating unit 22 generate a physical condition of theagent state as “hungry” based on determination that the agent is hungryas a result of the situation of late return and delay of a meal.

At input 2, the agent state estimating unit 21 and the user stateestimating unit 22 generate a situation of the agent state as “aftermeal”, an emotion value as “+1”, an emotion as “happy”, and a physicalcondition as “fully fed” based on input of a dynamic attribute“meal=everything eaten”.

At input 3 and input 4, the emotion of the agent state changes to“happy” as a result of input of a dynamic attribute “souvenir=food”.However, no dynamic attribute for changing the physical condition ispresent, wherefore the state of input 2 “physical condition=fully fed”continues. In this stage, there is no difference between input 3 andinput 4.

The utterance intention generating unit 3 determines an utteranceintention based on the current agent state and the agent statecontinuing from the past.

At input 1, the utterance intention generating unit 3 generates“loneliness expression” for house sitting based on the agent state“emotion=lonely”, and “hunger expression” based on “physicalcondition=hungry”. In addition, the utterance intention generating unit3 generates “user's joy sympathy” based on the user state“positive-negative state=positive”.

At input 2, the utterance intention generating unit 3 generates “joyexpression” based on the agent state “emotion=happy”, and generates“fully fed state expression” based on the agent state “physicalcondition=fully fed”.

At input 3 and input 4, the utterance intention generating unit 3generates “joy expression” based on the agent state “emotion=happy”, andgenerates “fully fed state expression” based on the agent state“physical condition=fully fed”. In this stage, there is still nodifference between input 3 and input 4.

The conversation sentence generating unit 4 defines such a conversationsentence generation rule as to touch upon previous contents withreference to history information on a dynamic attribute, an agent state,and a user state at a previous time.

At input 3, a sentence corresponding to the current agent state (fullyfed) is generated without referring to history information. However, atinput 4, the sentence to be generated is defined as a responseconsistent with the fact that the agent was “hungry” with reference tohistory information at a certain previous time designated by a dynamicattribute, as information indicating the agent state (hungry) at theprevious time. At input 4, “(input 1)” corresponding to a “historypointer” is given as a dynamic attribute, so that the agent state at thetime of input 1 stored in the agent state storing unit 61 can bereferred to based on this information. At the time of reference, the“physical condition” of the agent state at the previous time of input 1is referred to based on such a description as “history: A state−>physical condition”.

Accordingly, generation of a conversation sentence consistent with thepast is allowed based on a rule utilizing previous results of stateestimation.

TABLE 51 EXAMPLE OF USER ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME MAMMY GENDER FEMALE

TABLE 52 EXAMPLE OF AGENT ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUENICKNAME KORO GENDER MALE

TABLE 53 EXAMPLE OF DYNAMIC ATTRIBUTES ATTRIBUTE NAME ATTRIBUTE VALUE(INPUT 1) EMAIL LATE USER SITUATION DATING TIME ZONE EVENING (INPUT 2)MEAL EVERYTHING EATEN USER SITUATION HOME TIME ZONE NIGHT (INPUT 3)SOUVENIR FOOD USER SITUATION COMING HOME TIME ZONE NIGHT (INPUT 4)SOUVENIR FOOD HISTORY POINTER (INPUT 1) USER SITUATION COMING HOME TIMEZONE NIGHT

TABLE 54 EXAMPLE OF STATE ESTIMATION RULE (A STATE: AGENT STATE, USTATE: USER STATE) CONDITION ESTIMATION RESULT (INPUT 1) DYNAMICATTRIBUTE -> LATE A STATE -> SITUATION = HOUSE EMAIL = SITTING A STATE-> EMOTION VALUE = −1 A STATE -> EMOTION = LONELY A STATE -> PHYSICALCONDITION = HUNGRY DYNAMIC ATTRIBUTE -> DATING U STATE ->POSITIVE-NEGATIVE USER SITUATION STATE = POSITIVE U STATE -> SITUATION =DATING (INPUT 2) DYNAMIC EVERYTHING A STATE -> SITUATION = AFTERATTRIBUTE -> EATEN MEAL MEAL = A STATE -> EMOTION VALUE = +1 A STATE ->EMOTION = HAPPY A STATE -> PHYSICAL CONDITION = FULLY FED (INPUT 3, 4)DYNAMIC ATTRIBUTE -> FOOD A STATE -> SITUATION = WAITING SOUVENIR = FORSOUVENIR A STATE -> EMOTION = HAPPY A STATE -> EMOTION VALUE = +1

TABLE 55 EXAMPLE OF UTTERANCE INTENTION GENERATION RULE (A STATE: AGENTSTATE, U STATE: USER STATE) CONDITION INTENTION (INPUT 1) A STATE ->EMOTION = LONELY (SCORE: 4.0) LONELINESS EXPRESSION A STATE -> PHYSICALHUNGRY (SCORE: 2.0) HUNGER CONDITION = EXPRESSION U STATE -> POSITIVE(SCORE: 2.0) USER'S JOY POSITIVE-NEGATIVE SYMPATHY STATE = (INPUT 2) ASTATE -> EMOTION = HAPPY (SCORE: 4.0) JOY EXPRESSION A STATE -> PHYSICALFULLY FED (SCORE: 2.0) FULLY CONDITION = FED STATE EXPRESSION (INPUT 3,4) A STATE -> EMOTION = HAPPY (SCORE: 4.0) JOY EXPRESSION A STATE ->PHYSICAL FULLY FED (SCORE: 2.0) FULLY CONDITION = FED STATE EXPRESSION

TABLE 56 EXAMPLE OF CONVERSATION SENTENCE GENERATION RULE CONDITIONTEMPLATE (INPUT 1) UTTERANCE INTENTION = YOU ARE LATE! LONELINESSEXPRESSION (SCORE: SO LONELY WITH TEARS IN [A 4.0) ATTRIBUTE ->NICKNAME]'S A STATE -> SITUATION = HOUSE EYES! (T_T) SITTING UTTERANCEINTENTION = HUNGRY! HUNGRY! HUNGER EXPRESSION (SCORE: 4.0) UTTERANCEINTENTION = USER'S PHEW! (—.—;) JOY SYMPATHY (SCORE: 4.0) IT'S HARD, BUT[A ATTRIBUTE -> NICKNAME] WISHES YOU HAPPINESS! MAMMY (INPUT 2)UTTERANCE INTENTION = JOY I'M FULL! EXPRESSION (SCORE: 4.0) A STATE ->SITUATION = AFTER MEAL UTTERANCE INTENTION = FULLY SATISFIED! I CAN'TEAT ANY FED STATE EXPRESSION (SCORE: MORE! 2.0) (INPUT 3) GIVE FOODWITHOUT CONSIDERATION OF SITUATION AT INPUT 1 UTTERANCE INTENTION = JOYSOUVENIR? THANKS! EXPRESSION (SCORE: 4.0) A STATE -> SITUATION = WAITINGFOR SOUVENIR UTTERANCE INTENTION = FULLY OH! ({circumflex over( )}_{circumflex over ( )};) I'M COMPLETELY FULL FED STATE EXPRESSION(SCORE: NOW. WILL [A ATTRIBUTE -> 2.0) NICKNAME] GET FAT IF I EATSOUVENIR, TOO? (NERVOUS) (INPUT 4) GIVE FOOD TO AGENT WHICH WAS HUNGRYAT INPUT 1 UTTERANCE INTENTION = JOY SOUVENIR 

 SOUVENIR 

 THANKS 

EXPRESSION (SCORE: 4.0) A STATE -> SITUATION = WAITING FOR SOUVENIRUTTERANCE INTENTION = FULLY WELL, [A ATTRIBUTE -> FED STATE EXPRESSION(SCORE: NICKNAME] WAS HUNGRY JUST 2.0) BEFORE, BUT I'M FULL NOW. EVENSO, THERE'S ALWAYS ROOM FOR SNACK! I'LL TRY!

The state estimation rule, the utterance intention generation rule, andthe conversation sentence generation rule may be stored in a storingunit of the conversation-sentence generation device, for example, oranother device to which the conversation-sentence generation device isconnectable.

The present invention is applicable to a conversation system, a socialmedia service and the like which personify a non-human target such as ananimal and a machine and realize a conversation between a user and thepersonified target.

The conversation-sentence generation device according to the exemplaryembodiment of the present invention described herein may be practiced inthe form of an operation program or the like which is stored in astoring unit and read by a CPU (Central Processing Unit) to be executed,or may be constituted in the form of hardware. Alternatively, only apart of the functions discussed in the foregoing exemplary embodimentmay be practiced under a computer program.

A part or the whole of the foregoing exemplary embodiment may bedescribed as in the following supplemental notes, but is not limited tothese supplemental notes.

(Supplemental Note 1)

A conversation-sentence generation device that generates a conversationsentence of a virtual agent having a personified conversation with auser, including:

an input unit that receives, as input information, a conversationsentence given from the user to the agent, and clue information based onwhich a physical and psychological state of the agent is estimated;

an agent state storing unit that stores the physical and psychologicalstate of the agent as an agent state;

an agent state estimating unit that estimates a new agent state based onthe input information and the agent state;

an utterance intention generating unit that generates, based on theinput information and the agent state, an utterance intention directedfrom the agent to the user;

a conversation sentence generating unit that generates, based on theinput information, the agent state, and the utterance intention, aconversation sentence given from the agent to the user; and

an output unit that outputs the conversation sentence generated by theconversation sentence generating unit.

(Supplemental Note 2)

The conversation-sentence generation device according to SupplementalNote 1, wherein

the agent state estimating unit estimates a new agent state based on astate estimation rule that contains a state description part describingthe physical and psychological state of the agent, and a condition partdescribing a condition set for determining whether or not the agent isin the state described in the state description part with reference tothe input information and the agent state stored in the agent statestoring unit.

(Supplemental Note 3)

The conversation-sentence generation device according to SupplementalNotes 1 or 2, wherein

the utterance intention generating unit generates an utterance intentionbased on an utterance intention generation rule that contains anutterance intention description part describing an utterance intentiondirected from the agent to the user, and a condition part that describesa condition set for determining whether or not the agent is in theutterance intention described in the utterance intention descriptionpart with reference to the input information and the agent state.

(Supplemental Note 4)

The conversation-sentence generation device according to any one ofSupplemental Notes 1 to 3, wherein

the conversation sentence generating unit generates a conversationsentence based on a conversation sentence generation rule that containsa conversation sentence description part describing a conversationsentence given from the agent to the user, and a condition part thatdescribes a condition set for determining whether or not theconversation sentence described in the conversation sentence descriptionpart is appropriate for a conversation sentence given from the agent tothe user with reference to the input information, the agent state, andthe utterance intention.

(Supplemental Note 5)

The conversation-sentence generation device according to SupplementalNote 4, wherein

the conversation sentence generating unit prepares a plurality ofconversation sentence generation rules containing descriptions ofdifferent conversation sentences for an identical condition, and, so asto generate a different conversation sentence, preferentially selects aconversation sentence not used during an identical conversation wherecompletely the same input information, agent state, and utteranceintention are given a plurality of times.

(Supplemental Note 6)

The conversation-sentence generation device according to SupplementalNotes 4 or 5, wherein

the utterance intention generating unit generates an utterance intentionbased on an utterance intention generation rule that contains acondition including the agent state, and

the conversation sentence generating unit generates a conversationsentence based on a conversation sentence generation rule that containsa condition including the agent state to generate a conversationsentence corresponding to the agent state.

(Supplemental Note 7)

The conversation-sentence generation device according to any one ofSupplemental Notes 4 to 6, wherein

the agent state storing unit stores the agent state at a previous time,

the utterance intention generating unit generates an utterance intentionbased on an utterance intention generation rule that contains acondition including the agent state at the previous time, and

the conversation sentence generating unit generates the conversationsentence based on a conversation sentence generation rule that containsa condition including the agent state at the previous time.

(Supplemental Note 8)

A conversation-sentence generation method that generates a conversationsentence of a virtual agent having a personified conversation with auser, including:

receiving, as input information, a conversation sentence given from theuser to the agent, and clue information based on which a physical andpsychological state of the agent is estimated;

storing the physical and psychological state of the agent as an agentstate;

estimating a new agent state based on the input information and theagent state;

generating, based on the input information and the agent state, anutterance intention directed from the agent to the user;

generating, based on the input information, the agent state, and theutterance intention, a conversation sentence given from the agent to theuser; and

outputting the generated conversation sentence.

(Supplemental Note 9)

The conversation-sentence generation method according to SupplementalNote 8, wherein

estimating a new agent state based on a state estimation rule thatcontains a state description part describing the physical andpsychological state of the agent, and a condition part describing acondition set for determining whether or not the agent is in the statedescribed in the state description part with reference to the inputinformation and the agent state stored in the agent state storing unit.

(Supplemental Note 10)

The conversation-sentence generation method according to SupplementalNotes 8 or 9, wherein

generating an utterance intention based on an utterance intentiongeneration rule that contains an utterance intention description partdescribing an utterance intention directed from the agent to the user,and a condition part that describes a condition set for determiningwhether or not the agent is in the utterance intention described in theutterance intention description part with reference to the inputinformation and the agent state.

(Supplemental Note 11)

The conversation-sentence generation method according to any one ofSupplemental Notes 8 to 10, wherein

generating a conversation sentence based on a conversation sentencegeneration rule that contains a conversation sentence description partdescribing a conversation sentence given from the agent to the user, anda condition part that describes a condition set for determining whetheror not the conversation sentence described in the conversation sentencedescription part is appropriate for a conversation sentence given fromthe agent to the user with reference to the input information, the agentstate, and the utterance intention.

(Supplemental Note 12)

The conversation-sentence generation method according to SupplementalNote 11, wherein

preparing a plurality of conversation sentence generation rulescontaining descriptions of different conversation sentences for anidentical condition, and, so as to generate a different conversationsentence, preferentially selecting a conversation sentence not usedduring an identical conversation where completely the same inputinformation, agent state, and utterance intention are given a pluralityof times.

(Supplemental Note 13)

The conversation-sentence generation method according to SupplementalNotes 11 or 12, wherein

generating an utterance intention based on an utterance intentiongeneration rule that contains a condition including the agent state, and

generating a conversation sentence based on a conversation sentencegeneration rule that contains a condition including the agent state togenerate a conversation sentence corresponding to the agent state.

(Supplemental Note 14)

The conversation-sentence generation method according to any one ofSupplemental Notes 11 to 13, wherein

storing the agent state at a previous time,

generating an utterance intention based on an utterance intentiongeneration rule that contains a condition including the agent state atthe previous time, and

generating the conversation sentence based on a conversation sentencegeneration rule that contains a condition including the agent state atthe previous time.

(Supplemental Note 15)

A program allowing a computer to execute:

a process that receives, as input information, a conversation sentencegiven from a user to an agent, and clue information based on which aphysical and psychological state of the agent is estimated;

a process that stores the physical and psychological state of the agentas an agent state;

an agent state estimating process that estimates a new agent state basedon the input information and the agent state;

an utterance intention generating process that generates, based on theinput information and the agent state, an utterance intention directedfrom the agent to the user;

a conversation sentence generating process that generates, based on theinput information, the agent state, and the utterance intention, aconversation sentence given from the agent to the user; and

a process that outputs the conversation sentence generated by theconversation sentence generating process.

(Supplemental Note 16)

The program according to Supplemental Note 15, wherein

the agent state estimating process estimates a new agent state based ona state estimation rule that contains a state description partdescribing the physical and psychological state of the agent, and acondition part describing a condition set for determining whether or notthe agent is in the state described in the state description part withreference to the input information and the agent state stored in theagent state storing unit.

(Supplemental Note 17)

The program according to Supplemental Notes 15 or 16, wherein

the utterance intention generating process generates an utteranceintention based on an utterance intention generation rule that containsan utterance intention description part describing an utteranceintention directed from the agent to the user, and a condition part thatdescribes a condition set for determining whether or not the agent is inthe utterance intention described in the utterance intention descriptionpart with reference to the input information and the agent state.

(Supplemental Note 18)

The program according to any one of Supplemental Notes 15 to 17, wherein

the conversation sentence generating process generates a conversationsentence based on a conversation sentence generation rule that containsa conversation sentence description part describing a conversationsentence given from the agent to the user, and a condition part thatdescribes a condition set for determining whether or not theconversation sentence described in the conversation sentence descriptionpart is appropriate for a conversation sentence given from the agent tothe user with reference to the input information, the agent state, andthe utterance intention.

(Supplemental Note 19)

The program according to Supplemental Note 18, wherein

the conversation sentence generating process prepares a plurality ofconversation sentence generation rules containing descriptions ofdifferent conversation sentences for an identical condition, and, so asto generate a different conversation sentence, preferentially selects aconversation sentence not used during an identical conversation wherecompletely the same input information, agent state, and utteranceintention are given a plurality of times.

(Supplemental Note 20)

The program according to Supplemental Notes 18 or 19, wherin

the utterance intention generating process generates an utteranceintention based on an utterance intention generation rule that containsa condition including the agent state, and

the conversation sentence generating process generates a conversationsentence based on a conversation sentence generation rule that containsa condition including the agent state to generate a conversationsentence corresponding to the agent state.

(Supplemental Note 21)

The program according to any one of Supplemental Notes 18 through 20,including:

the agent state storing process stores the agent state at a previoustime,

the utterance intention generating process generates an utteranceintention based on an utterance intention generation rule that containsa condition including the agent state at the previous time, and

the conversation sentence generating process generates the conversationsentence based on a conversation sentence generation rule that containsa condition including the agent state at the previous time.

While a preferred exemplary embodiment according to the presentinvention has been described, the present invention is not necessarilylimited to the foregoing exemplary embodiment. Various modifications maybe made without departing from the scope of the technical spirit of thepresent invention.

This application claims priority to Japanese Patent Application No.2012-246261, filed Nov. 8, 2012, the entirety of which is herebyincorporated by reference.

REFERENCE SIGNS LIST

-   1 Input unit-   2 Agent state estimating unit-   3 Utterance intention generating unit-   4 Conversation sentence generating unit-   5 Output unit-   6 Agent state storing unit-   21 Agent state estimating unit-   22 User state estimating unit-   61 Agent state storing unit-   62 User state storing unit

What is claimed is:
 1. A conversation-sentence generation device thatgenerates a conversation sentence of a virtual agent having apersonified conversation with a user, comprising: an input unit thatreceives, as input information, a conversation sentence given from theuser to the agent, and clue information based on which a physical andpsychological state of the agent is estimated; an agent state storingunit that stores the physical and psychological state of the agent as anagent state; an agent state estimating unit that estimates a new agentstate based on the input information and the agent state; an utteranceintention generating unit that generates, based on the input informationand the agent state, an utterance intention directed from the agent tothe user; a conversation sentence generating unit that generates, basedon the input information, the agent state, and the utterance intention,a conversation sentence given from the agent to the user; and an outputunit that outputs the conversation sentence generated by theconversation sentence generating unit.
 2. The conversation-sentencegeneration device according to claim 1, wherein the agent stateestimating unit estimates a new agent state based on a state estimationrule that contains a state description part describing the physical andpsychological state of the agent, and a condition part describing acondition set for determining whether or not the agent is in the statedescribed in the state description part with reference to the inputinformation and the agent state stored in the agent state storing unit.3. The conversation-sentence generation device according to claim 1,wherein the utterance intention generating unit generates an utteranceintention based on an utterance intention generation rule that containsan utterance intention description part describing an utteranceintention directed from the agent to the user, and a condition part thatdescribes a condition set for determining whether or not the agent is inthe utterance intention described in the utterance intention descriptionpart with reference to the input information and the agent state.
 4. Theconversation-sentence generation device according to claim 1, whereinthe conversation sentence generating unit generates a conversationsentence based on a conversation sentence generation rule that containsa conversation sentence description part describing a conversationsentence given from the agent to the user, and a condition part thatdescribes a condition set for determining whether or not theconversation sentence described in the conversation sentence descriptionpart is appropriate for a conversation sentence given from the agent tothe user with reference to the input information, the agent state, andthe utterance intention.
 5. The conversation-sentence generation deviceaccording to claim 4, wherein the conversation sentence generating unitprepares a plurality of conversation sentence generation rulescontaining descriptions of different conversation sentences for anidentical condition, and, so as to generate a different conversationsentence, preferentially selects a conversation sentence not used duringan identical conversation where completely the same input information,agent state, and utterance intention are given a plurality of times. 6.The conversation-sentence generation device according to claim 4,wherein the utterance intention generating unit generates an utteranceintention based on an utterance intention generation rule that containsa condition including the agent state, and the conversation sentencegenerating unit generates a conversation sentence based on aconversation sentence generation rule that contains a conditionincluding the agent state to generate a conversation sentencecorresponding to the agent state.
 7. The conversation-sentencegeneration device according to claim 4, wherein the agent state storingunit stores the agent state at a previous time, the utterance intentiongenerating unit generates an utterance intention based on an utteranceintention generation rule that contains a condition including the agentstate at the previous time, and the conversation sentence generatingunit generates the conversation sentence based on a conversationsentence generation rule that contains a condition including the agentstate at the previous time.
 8. A conversation-sentence generation methodthat generates a conversation sentence of a virtual agent having apersonified conversation with a user, comprising: receiving, as inputinformation, a conversation sentence given from the user to the agent,and clue information based on which a physical and psychological stateof the agent is estimated; storing the physical and psychological stateof the agent as an agent state; estimating a new agent state based onthe input information and the agent state; generating, based on theinput information and the agent state, an utterance intention directedfrom the agent to the user; generating, based on the input information,the agent state, and the utterance intention, a conversation sentencegiven from the agent to the user; and outputting the generatedconversation sentence.
 9. A non-transitory computer-readable storagemedium storing a program causing a computer to execute: a process thatreceives, as input information, a conversation sentence given from auser to an agent, and clue information based on which a physical andpsychological state of the agent is estimated; a process that stores thephysical and psychological state of the agent as an agent state; aprocess that estimates a new agent state based on the input informationand the agent state; a process that generates, based on the inputinformation and the agent state, an utterance intention directed fromthe agent to the user; a conversation sentence generating process thatgenerates, based on the input information, the agent state, and theutterance intention, a conversation sentence given from the agent to theuser; and a process that outputs the conversation sentence generated bythe conversation sentence generating process.